Large language models (LLMs) have achieved remarkable progress in many
natural language processing tasks. However, our experiment reveals that, in
stance detection tasks, LLMs may generate biased stances due to spurious
sentiment-stance correlation and preference towards certain individuals and
topics, thus harming their performance. Therefore, in this paper, we propose to
Mitigate Biases of LLMs in stance detection with Calibration (MB-Cal). In
which, a novel gated calibration network is devised to mitigate the biases on
the stance reasoning results from LLMs. Further, to make the calibration more
accurate and generalizable, we construct counterfactual augmented data to
rectify stance biases. Experimental results on in-target and zero-shot stance
detection tasks show that the proposed MB-Cal can effectively mitigate biases
of LLMs, achieving state-of-the-art results.

在这篇论文中，我们提出了一种新颖的门控校准网络来减轻大型语言模型在态度检测中的偏见，通过构建反事实增强数据来纠正态度偏见，实验结果表明，提出的 MB-Cal 方法可以有效地减轻大型语言模型的偏见，达到最先进的结果。